FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4224952055> ?p ?o ?g. }

• W4224952055 endingPage "100315" @default.
• W4224952055 startingPage "100315" @default.
• W4224952055 abstract "This study pertains to the usage and effectiveness of the fuzzy time series (FTS) models and machine learning methods in forecasting movements of financial data. The datasets used in this study are the actual closing prices and transaction volumes of 9 different cryptocurrencies, from the earliest time obtainable in Yahoo! Finance, all the way to 31 Oct 2021. Firstly, this paper presents a study of the severe drawbacks of all existing literature on FTS. In particular, this article outlines severe shortcomings of all existing FTS based algorithms that caused inaccuracies among all existing FTS-based algorithms in yielding meaningful prediction. Then, a novel structure of our improvised FTS, denoted as QFTS, is presented in this paper, which aims to rectify all flaws exist in all conventional FTS based models in literature. A further hybrid of QFTS with ANN is also presented. Later, a comparative analysis of all the aforementioned FTS models is presented in terms of overall forecasting accuracy and forecasting accuracy under specific conditions. The results are being compared in terms of MAPE. The newly invented QFTS model and the QFTS-ANN hybrid is found to profoundly outperform all the existing FTS models in literature, which includes Singh's FTS model. Such innovation profoundly rectifies severe shortcomings in financial forecasting that have persisted for many years in the past literature." @default.
• W4224952055 created "2022-04-28" @default.
• W4224952055 creator A5008082871 @default.
• W4224952055 creator A5013758736 @default.
• W4224952055 creator A5056943300 @default.
• W4224952055 creator A5059086458 @default.
• W4224952055 creator A5070517330 @default.
• W4224952055 creator A5075327643 @default.
• W4224952055 date "2022-05-01" @default.
• W4224952055 modified "2023-10-18" @default.
• W4224952055 title "A New Hybrid Model of Fuzzy Time Series and Genetic Algorithm Based Machine Learning Algorithm: A Case Study of Forecasting Prices of Nine Types of Major Cryptocurrencies" @default.
• W4224952055 cites W1971619052 @default.
• W4224952055 cites W1971869067 @default.
• W4224952055 cites W1992096620 @default.
• W4224952055 cites W1995461802 @default.
• W4224952055 cites W2004157188 @default.
• W4224952055 cites W2017954768 @default.
• W4224952055 cites W2019207321 @default.
• W4224952055 cites W2026235818 @default.
• W4224952055 cites W2036156437 @default.
• W4224952055 cites W2040870580 @default.
• W4224952055 cites W2052011703 @default.
• W4224952055 cites W2081874651 @default.
• W4224952055 cites W2116911268 @default.
• W4224952055 cites W2131453387 @default.
• W4224952055 cites W2150198213 @default.
• W4224952055 cites W2168577773 @default.
• W4224952055 cites W2297745799 @default.
• W4224952055 cites W2583358500 @default.
• W4224952055 cites W2766380553 @default.
• W4224952055 cites W2770063219 @default.
• W4224952055 cites W2803195983 @default.
• W4224952055 cites W2806777472 @default.
• W4224952055 cites W2884603773 @default.
• W4224952055 cites W2890763248 @default.
• W4224952055 cites W2892579226 @default.
• W4224952055 cites W2897880840 @default.
• W4224952055 cites W2898791292 @default.
• W4224952055 cites W2900785697 @default.
• W4224952055 cites W2902141129 @default.
• W4224952055 cites W2908182041 @default.
• W4224952055 cites W2910062285 @default.
• W4224952055 cites W2923169900 @default.
• W4224952055 cites W2945346514 @default.
• W4224952055 cites W2950376465 @default.
• W4224952055 cites W3040379296 @default.
• W4224952055 cites W4241443503 @default.
• W4224952055 cites W4313498487 @default.
• W4224952055 doi "https://doi.org/10.1016/j.bdr.2022.100315" @default.
• W4224952055 hasPublicationYear "2022" @default.
• W4224952055 type Work @default.
• W4224952055 citedByCount "3" @default.
• W4224952055 countsByYear W42249520552023 @default.
• W4224952055 crossrefType "journal-article" @default.
• W4224952055 hasAuthorship W4224952055A5008082871 @default.
• W4224952055 hasAuthorship W4224952055A5013758736 @default.
• W4224952055 hasAuthorship W4224952055A5056943300 @default.
• W4224952055 hasAuthorship W4224952055A5059086458 @default.
• W4224952055 hasAuthorship W4224952055A5070517330 @default.
• W4224952055 hasAuthorship W4224952055A5075327643 @default.
• W4224952055 hasConcept C11413529 @default.
• W4224952055 hasConcept C119857082 @default.
• W4224952055 hasConcept C124101348 @default.
• W4224952055 hasConcept C143724316 @default.
• W4224952055 hasConcept C151406439 @default.
• W4224952055 hasConcept C151730666 @default.
• W4224952055 hasConcept C154945302 @default.
• W4224952055 hasConcept C180706569 @default.
• W4224952055 hasConcept C38652104 @default.
• W4224952055 hasConcept C41008148 @default.
• W4224952055 hasConcept C75684735 @default.
• W4224952055 hasConcept C86803240 @default.
• W4224952055 hasConceptScore W4224952055C11413529 @default.
• W4224952055 hasConceptScore W4224952055C119857082 @default.
• W4224952055 hasConceptScore W4224952055C124101348 @default.
• W4224952055 hasConceptScore W4224952055C143724316 @default.
• W4224952055 hasConceptScore W4224952055C151406439 @default.
• W4224952055 hasConceptScore W4224952055C151730666 @default.
• W4224952055 hasConceptScore W4224952055C154945302 @default.
• W4224952055 hasConceptScore W4224952055C180706569 @default.
• W4224952055 hasConceptScore W4224952055C38652104 @default.
• W4224952055 hasConceptScore W4224952055C41008148 @default.
• W4224952055 hasConceptScore W4224952055C75684735 @default.
• W4224952055 hasConceptScore W4224952055C86803240 @default.
• W4224952055 hasLocation W42249520551 @default.
• W4224952055 hasOpenAccess W4224952055 @default.
• W4224952055 hasPrimaryLocation W42249520551 @default.
• W4224952055 hasRelatedWork W1964982224 @default.
• W4224952055 hasRelatedWork W2080650820 @default.
• W4224952055 hasRelatedWork W2150798635 @default.
• W4224952055 hasRelatedWork W2354329565 @default.
• W4224952055 hasRelatedWork W2357235357 @default.
• W4224952055 hasRelatedWork W2386131991 @default.
• W4224952055 hasRelatedWork W2961085424 @default.
• W4224952055 hasRelatedWork W2995778467 @default.
• W4224952055 hasRelatedWork W3014300295 @default.
• W4224952055 hasRelatedWork W4205100482 @default.
• W4224952055 hasVolume "28" @default.

• next